Wind Stress Working Group 2015 IOVWST Meeting Portland, OR

Transcription

1 Wind Stress Working Group 2015 IOVWST Meeting Portland, OR Summary of Research Topics, Objectives and Questions James B. Edson University of Connecticut SPURS Mooring, Farrar, WHOI

2 Background Motivation for the working group can be found in a recent ocean flux remote sensing survey paper by Bourassa et al. (2010 TOS): Recent studies find that scatterometers, and presumably other wind-sensing instruments, respond to stress rather than wind, accounting for variability due to wind, buoyancy, surface currents, waves, and air density. The basis for this is that radar backscatter is proportion to surface roughness, and we generally assume that surface roughness is most closely correlated with wind stress,. Wind stress is most closely correlated with the equilalent neutral wind speed (squared) relative to the sea surface, computed at a height of 10-m, U r10n. The relationship between U r10n and given found using a neutral drag coefficient C D10N : ln( / 0) uw C U U C a a D N r N r N D N z z Therefore, the stress can be estimated from scatterometer-derived winds through a drag coefficient without the need for stability corrections.

3 Stress will be nominate as an Essential Climate Variable Ocean Observation Panel for Climate update by Mark Bourassa

4 What Qualifies as an EOV or ECV? ECV = Essential Climate Variable EOV = Essentail Ocean Variable Essential variables have the following characteristics Relevance: Important for monitoring the variability of the ocean (or the climate system for ECVs) Feasible: Technically able to measure at sufficient accuracy Cost Effective: able to support the cost of the observations Feasibility and Cost Effectiveness are also critical to get buy in from funders of the observing system (not just Relevance) Furthermore, the goal is to measure a few carefully selected variables very well, rather than try to measure every variable that is relevant to climate

5 Preliminary Objectives Improved estimates of wind stress derived from scatterometer estimates of the equivalent neutral wind via a WSWG recommended drag coefficient. Investigate the need for more direct estimates of wind stress from scatterometer measurements of backscatter: f ( 0,...) CLIMODE 2006

6 Preliminary Objectives Improved estimates of wind stress derived from scatterometer estimates of the equivalent neutral wind via a WSWG recommended drag coefficient. Investigate the need for more direct estimates of wind stress from scatterometer measurements of backscatter: f ( 0,...) CLIMODE 2006 SPURS 2012/13

7 Preliminary Objectives Improved estimates of wind stress derived from scatterometer estimates of the equivalent neutral wind via a WSWG recommended drag coefficient. Investigate the need for more direct estimates of wind stress from scatterometer measurements of backscatter: f ( 0,...) CLIMODE 2006 SPURS 2012/13

8 Preliminary Objectives Improved estimates of wind stress derived from scatterometer estimates of the equivalent neutral wind via a WSWG recommended drag coefficient. Investigate the need for more direct estimates of wind stress from scatterometer measurements of backscatter: f ( 0,...) COARE3.5 w/ ECMWF QSCAT w/ L&P Hormann & Centurioni

9 DCFS Combined Datasets Expanded Wave Conditions CLIMODE Coastal NE - bifurcation in steepness SPURS I N. Atlantic 24 N

10 Stress-equivalent Winds, U10S Equivalent neutral winds, u r10n, depend only on u *, surface roughness and the presence of ocean currents and were used for backscatter geophysical model functions (GMFs) Stress-equivalent wind is a better input for backscatter GMFs: a u ur10n r10s a Implemented in MyO FO v5 and under evaluation in the IOVWST Final Meeting Palma de Mallorca, March 2015

11 Active Whitecap Coverage Estimates Directly from QuikSCAT L1B σ 0 Measurements HH σ 0 (db) VV σ 0 (db) Aaron C. Paget, Ph.D. BYU - MERS Laboratory Whitecaps (W) are part of the reported scatterometer signal. We can estimate of W with L1B σ 0. Traditional: σ 0 -> wind GMF -> Wind -> Whitecap Parameterization -> Whitecap Estimate New Approach: σ 0 -> whitecap GMF -> Whitecap Estimate Details The traditional approach propagates estimation errors New approach bypasses determining the wind speed and estimates W directly Whitecap GMF only requires input from data available in the L1B QuikSCAT recorded The signal strength varies azimuthally with respect to wind direction Preliminary results identify potential for reducing whitecap estimate uncertainties Whitecap Percentage % 3% 2% 1% Wind Normalized Observation Angle 0.2% 0.1% 0.01% Whitecap Percentage % 3% 2% 1% Wind Normalized Observation Angle 0.2% 0.1% 0.01%

12 Surface Stress and Roughness at High Winds? Refinement of DC Stress Measurements How do we quantify the behavior at High Winds?

13 Summary of Research Issues The following issues have all been considered by the IOVWSTs. The IOVWSTs have a good handle on some of them and significant disagreement or overall lack of understanding exists with other. Shorter wind-waves matter as they support a significant fraction of the surface stress and provide the roughness elements for scatterometers. Surface stress is an essential variable as it drives these wind-waves. Stability matters as it modulates the momentum flux Air-density matters as it is a key component of the momentum flux a uw Sea-surface temperature, viscosity and tension matter as they govern the surface stress Questions addressed in the following talks: What is the behavior of the surface stress and roughness at extreme winds (> 20 m/s)? What is the role of longer waves on surface stress modulation and the geometry of the sea surface seen by scatterometers and radiometers? How does variability across the flux and radar footprints matter in all of the above variables affect wind retrievals?

14 Summary of Session So Far Questions addressed in the following talks: What is the behavior of the surface stress and roughness at extreme winds (> 20 m/s)? Interpretation of dropwinsonde data remains an issue, e.g., an approach that utilized a displacement height was presented to estimate both u * and U 10N. An approach that utilized Scatterometer data alone with previous parameterizations was presented, which provided reasonable drag coefficients at exteme winds. An Extreme Wind Workshop to discuss these and other options is warranted. How do we consistently address air density in satellite wind retrievals? Measurements do show an effect but its not consistent across platforms and products. There is a need to determine how the GMF (reference wind) for the various products are trained and it would be useful to identify a POC for each product to help sort out the dependence on density, SST and viscosity. What is the role of longer waves on surface stress modulation and the geometry of the sea surface seen by scatterometers and radiometers? Some dependence of the drag coefficient on surface slope of longer waves has been seen for wind speeds below ~ 7 m/s. The satellite wind products show good agreement with buoy measurements in high-wind, fetch-limited conditions except very near the coast. What groups are producing stress products? Came we develop and recommend a consensus drag coefficient for stress retrievals (including extreme winds)? How does variability across the flux and radar footprints matter in all of the above variables affect wind retrievals?

15 Time for Talks